Imaging-derived biomarkers from (68)Ga-DOTATOC PET/CT scans to predict survival of patients with neuroendocrine tumors after PRRT with (177)Lu-DOTATATE

利用 (68)Ga-DOTATOC PET/CT 扫描的影像衍生生物标志物预测接受 (177)Lu-DOTATATE 肽受体放射性核素治疗 (PRRT) 的神经内分泌肿瘤患者的生存期。

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Abstract

BACKGROUND: Neuroendocrine tumors have increased in prevalence and diversity in recent years and are often diagnosed at metastatic stages. Compared with nonradioactive systemic treatment with somatostatin analogs, peptide receptor radionuclide therapy (PRRT) has shown superior overall survival benefits for well-differentiated neuroendocrine tumor patients. This study aimed to identify biomarkers from (68)Ga‒DOTATOC PET/CT scans to predict survival in patients treated with PRRT in the clinic. METHODOLOGY: This retrospective study analyzed (68)Ga-DOTATOC PET/CT data from 67 NET patients undergoing PRRT. Tumor volumes and SUV metrics were segmented using standardized protocols. Radiomics features from liver metastases were extracted and preprocessed for analysis. Data were analysed via Kaplan-Meier, Cox regression, and PCA to evaluate the prognostic value of volumetric-, radiomics-, and clinicopathological parameters. RESULTS: This study included scans from 67 patients with an average age of 67 years. The mean survival time was 46.5 months, with 43% of patients alive or lost to follow-up at the conclusion of data collection. Despite comprehensive analyses, neither volumetric parameters, including total tumor volume and organ-specific tumor volume, nor SUV values (SUVmax and SUVmean) were robust predictors of overall survival. K‒M and Cox regression analyses revealed no significant differences in survival between the high- and low-risk groups for these parameters. Furthermore, radiomics features extracted from liver metastases did not demonstrate significant prognostic value. CONCLUSION: Quantification of (68)Ga-DOTATOC PET/CT-derived parameters offers limited prognostic value for OS in NET patients who are receiving PRRT in clinical practice. These findings might emphasize the current robust integration of imaging in clinical decision-making for NET management.

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